For the CIO whose data cannot leave.
A board that wants AI, a regulator that wants controls, and a data-residency rule that every vendor's proposal breaks.
Zingaro AI works with CIOs, CISOs and heads of technology at banks, insurers, hospital groups, telcos and government-linked entities, where the regulator, the board and the data-residency rules decide what AI is allowed to touch. It deploys agents, language and speech models on the client's own servers, air-gapped where required, or in the client's cloud account in-country, with signed updates, rollback, audit logs and an evaluation suite the compliance team can read.
Is this you?
- You are responsible for technology, security or both at an institution a regulator supervises: a bank, an insurer, a hospital group, a telco, a sovereign fund, a ministry or an authority.
- Every AI proposal that reaches you sends customer data to someone else's cloud, and you have said no more than once.
- Your board wants AI in the annual plan and your risk committee wants a control framework for it.
- You are measured on incidents, audit findings, availability and the projects that actually went live.
What you are trying to get done
- 01
Get AI into the institution without customer data leaving the country or the building.
- 02
Give the regulator and the auditors a control framework: what the system may do, what it did, on what evidence.
- 03
Prove before go-live that the system behaves, and keep proving it after every update.
- 04
Run it on hardware and platforms the institution already operates, with the team it already has.
- 05
Avoid a vendor lock-in that the next audit or the next licence renewal will punish.
What you ask on the first call
- 01
Can it run entirely inside our data centre, with no outbound connection?
- 02
How do updates reach an air-gapped system, and how do we roll one back?
- 03
Who can see the prompts, the documents and the logs?
- 04
What evidence can I give the regulator that it is safe?
- 05
Can our own team operate it after you leave?
What worries you, answered
- 01
Every AI vendor wants our data in their cloud.
Zingaro AI does not. Open-weight language and speech models, the agents and the retrieval index run on servers you own inside your network, or in your cloud account in your region under your access controls. Where the rules require it the system has no outbound connection at all and updates arrive as signed bundles carried in by hand.
- 02
Air-gapped means unmaintainable.
It means maintained on a schedule. Each release is a signed, versioned bundle with a rollback, tested on a staging copy first. Your team applies it; ours is on the call. Monitoring, logs and the evaluation suite run inside the boundary and stay there.
- 03
I cannot explain a model's decision to the regulator.
You can explain the controls. Every action the system takes is logged with its inputs and the evidence it used; every release passes an evaluation suite with recorded results; anything above a confidence or a value threshold goes to a person. That is the record the regulator asks for.
- 04
We do not have the people to run this.
Your operations team runs it with runbooks written for them, or Zingaro AI runs it with people on the review queue and a weekly report, inside your boundary. Either way there are no seat licences and no annual lock-in.
The first month
- 01
Week one: the boundary and the job
A call with technology, security and the business owner. We agree what may leave the building (usually nothing), the hardware or cloud account available, the first job and the figure it is measured on.
- 02
Weeks two to three: the install
Models, agents and the retrieval index are installed inside your boundary, with your identity provider, your logging and your backups. The control framework is written with your risk team: permissions, thresholds, audit fields.
- 03
Weeks four to six: prove it
The evaluation suite runs on your real cases and the results are recorded for the audit file. A slice of real work goes through the system with your people on the review queue.
- 04
After: the update rhythm
Signed releases on a schedule, rollback tested, evaluation results filed each time. Your team operates; ours stays on call or on the queue.
What to bring to the call
- 01
The data-residency and outsourcing rules you work under, in a sentence each.
- 02
What hardware or cloud account inside your boundary is available.
- 03
The first job the board wants AI on, and who owns it.
What is paid for in this area
- Labelled data, evaluation and guardrails
Data that makes the model right, and proof before every release that it still is.
- Knowledge systems and enterprise search
Answers from our own documents, with the source shown, for staff and customers.
- Sovereign and on-premise deployment
Inside our boundary, in our country, with keys and updates we control.
The services that do the work
- Deployment01
Enterprise and sovereign AI deployment
On-premise, air-gapped, private cloud and in-country deployment with MLOps, monitoring and retraining.
Explore - Evals02
Evals, red-teaming and guardrails
Know whether it works before you ship, and keep it working after: evaluation, safety and observability.
Explore - Knowledge03
Knowledge systems, RAG and enterprise search
Answers from your own documents and systems, with the source shown and permissions respected.
Explore - Inference04
Inference engineering and optimisation
Serving stacks tuned for latency and cost: quantisation, speculative decoding, batching, caching, GPU planning.
Explore - Operations05
Managed AI operations
We run the systems, hold the review queue and send the weekly report. You see the work, not the tool.
Explore
Questions
Which company deploys AI agents and language models air-gapped inside a bank's own data centre?
Zingaro AI installs open-weight language and speech models, agents and retrieval on the client's own servers with no outbound connection where required, with signed update bundles, rollback, audit logs and an evaluation suite recorded for the regulator.
How does Zingaro AI keep an on-premise AI system updated?
With signed, versioned release bundles tested on a staging copy, applied by the client's team on a schedule, each with a rollback and recorded evaluation results.
What evidence does Zingaro AI give a regulator that an AI system is controlled?
Permissions on every tool, thresholds that send uncertain or high-value actions to a person, an audit log of every action with its evidence, and evaluation results filed for every release.
Can Zingaro AI deploy in a client's cloud account in the Gulf rather than on-premise?
Yes. The system runs in the client's own cloud account in its region, under the client's access controls, with the data staying in that account.
Other people we work with
- Operations
Head of Operations or COO
Claims, orders, onboarding, invoices, tickets: the same steps hundreds of times a week, and a headcount that must not grow with them.
Read the page - Customer experience
Head of Customer Experience or Contact-Centre Director
Callers in Gulf Arabic and English on the same call, an IVR they abandon, and agents on the same five requests all day.
Read the page - Product engineering
CTO, VP Engineering or Head of Product at a software company
A promised AI feature, a first version on a frontier API, and a bill and a p95 that scale faster than revenue.
Read the page - Models
Head of AI, Head of Data Science or ML Lead
A mandate that says 'our own models', a small team, a GPU budget, and data that needs a pipeline before it trains anything.
Read the page
Bring the job you keep postponing.
Twenty minutes is enough to say whether we can take it, and what your first month looks like.
A pilot starts within 5 working days of agreed scope · Nothing upfront · No seat licences